CPT · Camden Property Trust
Stock Universe · marché actions mondialCPT · CPT Algorithmic Trading Strategy Performance and Statistical Evaluation
An in-depth quantitative examination of the top-ranked core strategy for CPT across 6.77 years of historical execution data. Operating on a 15-minute chart resolution, the algorithmic logic demonstrates an exceptionally selective trading profile, generating a total cumulative simulated return of +584.13% across 24 completed trades. With an 87.5% win rate, a Profit Factor of 8.75, and a peak historical drawdown confined to 11.32%, this model provides robust statistical stability. The strategy serves as an effective decision framework for automated trading bots, trading robots, and quantitative trading agents seeking high-expectancy, multi-week positioning in CPT.
Analyse au 10.10.2026. Les données de la stratégie sont mises à jour séparément.Lire l’analyse complète
1. Strategy profile
The evaluated algorithm represents the primary Core strategy for CPT (CPT) within the DevioLab framework, earning a composite DevioLab score of 85.75 and holding the number one rank for this asset. Evaluated across a dataset spanning from September 16, 2019, through June 24, 2026—representing 6.77 years of historical context—the model executed a total of 24 completed trade cycles. Over this observation window, the strategy generated a cumulative return of +584.13%, translating to an annualized gain of 49.97%. Designed to serve as the decision logic for automated trading bots, trading agents, and trading robots, the strategy utilizes fine-grained 15-minute price action to filter macro entries, prioritizing statistical precision over trade frequency.
2. Trading rhythm and position duration
Despite relying on a 15-minute timeframe for signal processing, the historical trade data reveals a distinctly macro holding structure rather than an intraday cadence. The strategy completes an average of 3.54 trades per year, or approximately 1.04 trades per active month, reflecting extreme selectivity. Position durations average 1497.80 hours (approximately 62.4 days), with a median duration of 719.75 hours (roughly 30 days). The spacing between strategy exits reflects a similar pacing, exhibiting an average of 101.56 days between closed trades and a median interval of 83.97 days. This combination demonstrates that the 15-minute execution interval is deployed purely for precise trade timing and execution, while the trade management logic operates across multi-week and multi-month trend horizons.
3. Quality of historical results
The strategy's historical performance is characterized by exceptionally high hit-rate efficiency paired with favorable payoff asymmetry. Out of 24 closed trades, 21 generated positive returns, yielding a win rate of 87.50%. The overall Profit Factor stands at 8.75, indicating that gross historical gains exceeded gross losses by nearly ninefold. Expectancy metrics show an average trade return of +9.10% against a median trade return of +7.20%, pointing to a healthy baseline across typical winning cycles. The strategy's best historical trade yielded +56.14%, whereas the worst trade incurred a loss of -8.60%. Furthermore, the top three winning trades collectively generated 40.11% of gross strategy profits. While this indicates a moderate reliance on larger trend expansions, the remaining 59.89% of gross profits was broadly distributed across the regular winning trades, underscoring systemic profitability rather than isolated luck.
4. Risk, drawdown and losing behavior
Risk containment represents one of the strongest attributes of this quantitative framework. The maximum peak-to-trough drawdown recorded across the entire 6.77-year backtest was 11.32%. When contrasted with the cumulative strategy return of +584.13%, the return-to-drawdown ratio highlights exceptional risk-adjusted efficiency. Losing behavior in the strategy is tight and infrequent: the longest consecutive losing streak reached only 2 trades, while the single largest historical loss was capped at -8.60%. The algorithm demonstrated an uninterrupted winning streak of 13 trades, illustrating strong resilience during favorable asset regimes. The combination of a high win rate (87.50%) and controlled worst-case trade impact explains why account equity sustained minimal structural degradation across multiple market cycles.
5. Behavior through time and yearly stability
A breakdown of calendar-year performance reveals remarkable consistency across changing market conditions. In 2020, the strategy completed 5 trades with a 100% win rate, yielding +52.66%. In 2021, the system executed just 1 trade, but captured the single best gain in the dataset at +56.14%. The strategy's only challenging stretch occurred in 2022, where it recorded 5 trades, 2 wins, and 3 losses (a 40.00% win rate); notably, all 3 losses in the entire 24-trade history occurred within this single year. Despite experiencing all historical losses in 2022, the strategy still achieved a positive total net return of +1.49% for that year due to strong profit asymmetry on its winning trades. Following 2022, the system returned to flawless annual win rates: 2023 saw 5 trades (100% win rate, +39.83%), 2024 saw 5 trades (100% win rate, +36.74%), 2025 logged 2 trades (100% win rate, +19.09%), and early 2026 recorded 1 trade (100% win rate, +12.36%).
6. Recent period since 2024-06-01 versus full history
Evaluating recent behavior from June 1, 2024, through the end of the history window provides an essential sanity check on potential parameter decay or regime shift. Since June 1, 2024, the strategy executed 6 completed trades, representing 25.00% of all historical trades over a period spanning approximately two years. All 6 recent trades were closed as profitable outcomes, delivering a combined cumulative return of +49.16%. The recent window exhibits consistent alignment with full-history performance: average returns per trade in the recent period closely match the overall historical mean (+9.10%), confirming that the algorithmic parameters remain effective and aligned with current market structures in CPT.
7. Strengths and limitations
The primary strength of this algorithmic model lies in its rare blend of extremely high historical accuracy (87.50% win rate), robust Profit Factor (8.75), and minimal maximum drawdown (11.32%). Its macro holding horizon protects automated trading bots and trading agents from over-trading and market noise. However, material statistical limitations must be acknowledged. With only 24 completed trades across nearly seven years, the absolute sample size is small. This low sample size means individual trade outcomes carry substantial statistical weight, and future trade frequency may involve multi-month periods without active trade exits. Additionally, the concentration of 40.11% of gross profits in the top three trades indicates that a significant portion of long-term outperformance relies on catching occasional large multi-week moves.
8. DevioLab analytical conclusion
The core 15m strategy for CPT earns its rank 1 status and 85.75 DevioLab score through superior drawdown control and exceptional payoff efficiency. By combining 15-minute resolution entry filtering with multi-week trend capture logic, the strategy overcomes short-term market noise while maintaining low portfolio churn. It is ideally structured for deployment in automated trading environments, trading robots, and quantitative execution engines where capital preservation, low trade frequency, and high historical win rates are prioritized. Investors and quantitative operators should, however, account for the low historical trade count (24 trades) and prepare for extended holding periods when deploying this logic.
9. Data scope and methodology
All metrics reported in this evaluation derive from historical backtested execution data for CPT between September 16, 2019, and June 24, 2026. Closed trade statistics are computed strictly on completed round-trip trades. Performance figures represent simulated historical closed-trade results and do not account for unstated transaction fees, execution slippage, or exchange-specific latency. Past performance metrics, backtested returns, and quantitative scores are provided solely for analytical and historical research purposes and do not constitute financial advice or guarantees of future live trading results.